Controls on evapotranspiration (ET) and its seasonality in select land surface models In Support of The LBA- Model Intercomparison Project (MIP) Brad Christoffersen University of Arizona

Motivation Amazonian forests are a locus for potential positive feedback to climate change. (Betts et al. 2004) Rising atmospheric CO 2 Drought frequency Forest dieback Stomatal closure

Motivation Amazonian forests are a locus for potential positive feedback to climate change (Betts et al. 2004) Land surface models have typically predicted water-limited control on ET across the seasonally dry Amazon (Shuttleworth 1991, Bonan et al. 1998, Dickinson et al. 2006) Rising atmospheric CO 2 Drought frequency Forest dieback Stomatal closure

Motivation Amazonian forests are a locus for potential positive feedback to climate change (Betts et al. 2004) Land surface models have typically predicted water-limited control on ET across the seasonally dry Amazon (Shuttleworth 1991, Bonan et al. 1998, Dickinson et al. 2006) Recent eddy tower syntheses reveal strong net radiation and little precipitation control on ET (Hasler and Avissar 2007, Juarez et al. 2007, Fisher et al. in press) Rising atmospheric CO 2 Drought frequency Forest dieback Stomatal closure

LBA-MIP Participating Models Ecosystem Process Models: –SSiB2, SiB2, SiB3, SiB-CASA, Biome-BGC, VISIT Dynamic Vegetation Models: –LPJ, HyLand, Jules-TRIFFID, CLM-DGVM, Orchidee, IBIS, LM3V Parameter Models: –5PM, SPA-DALEC Strictly Soil-Vegetation-Atmosphere Model: –NOAH Corresponding GCMs or Mesoscale Models: –CSU-SiB3, SPEEDY-LPJ, HadCM3-Jules, CCSM-CLM, IPSL-CM4- Orchidee, Eta-NOAH

Water Dynamics in Land Surface Models: Central Questions How do model-predicted seasonal / diurnal patterns in ET intercompare with each other and with data? What is the relative importance of radiation and available soil moisture as controls on ET? Long-term goal  Identify key model mechanisms associated w/ model-model and model-data differences

Tapajós K67 Site

Net Radiation Controls on ET – Observed and Modeled SiteSeasonSlope. HA Slope. MIP Intcpt. HA Intcpt. MIP R 2.HAR 2.MIP FNSWet Dry K34Wet Dry K67Wet Dry K83Wet Dry RJAWet Dry

Net Radiation Controls on ET – Observed and Modeled

SiteSeasonSlope. HA Slope. MIP Intcpt. HA Intcpt. MIP R 2.HAR 2.MIP K34Wet Dry K67Wet Dry K83Wet Dry RJAWet Dry FNSWet Dry Adapted from Hasler and Avissar 2007

Net Radiation Controls on ET – Observed and Modeled SiteSeasonSlope. HA Slope. MIP Intcpt. HA Intcpt. MIP R 2.HAR 2.MIP K34Wet Dry K67Wet Dry K83Wet Dry RJAWet Dry FNSWet Dry Adapted from Hasler and Avissar 2007

Varying Strength of Rnet control on ET across models (wet season) Mean Daily Net Radiation (W m-2) Mean Daily LE (W m-2) CLM3.5 (x4) SiB3 IBIS LPJ 1:1 MIP LSR H&A LSR

Most Models Lag ~2hrs behind observed diurnal cycles in LE flux Hour Hourly LE (W m-2) CLM3.5 (x4) SiB3 IBIS Data

Conclusions Considerable cross-model variance in predicted daily (and seasonal) patterns of ET Diurnal cycles of ET often lag those observed in data Controls on ET - Models in Semi-Agreement: –Net radiation exhibits dominant control on ET in absence of water stress –Increased soil moisture storage capacity in models  shifts ET peak to dry season (in phase with net radiation)

Future Directions What model mechanisms give rise to these differences? What is the quantitative partitioning of relative controls of radiation and soil moisture on ET? Explore empirical bucket model capability of caputuring seasonal and interannual variability in modeled soil moisture.

Thanks! The LBA-MIP Team Gustavo NASA Brad UofA Scott UofA Julio INPA Lindsey UT-Austin Margriet Vrije U. Laura Marcos UFV Fanny Potsdam Natalia UofA David Edinburgh Ian, CSU Ben, Potsdam Hewlley UFV